{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting credit default\n",
    "This dataset includes 30000 observations and whether or not they defaulted on their credit card. Observations include data such as credit limit, age, sex, highest education reached, and marital status. The dataset was obtained from the Tests section of Yellowbrick."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import zipfile\n",
    "import requests\n",
    "from sklearn.svm import LinearSVC\n",
    "import numpy.matlib\n",
    "from yellowbrick.classifier import ROCAUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def add_column(data_array,new_column):\n",
    "    nc = numpy.reshape(new_column,(-1,1))\n",
    "    if data_array is None:\n",
    "        return nc\n",
    "    else:\n",
    "        return numpy.concatenate((data_array,nc),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def add_categorical(data_array,new_column,datatype):\n",
    "    nc = numpy.reshape(new_column,(-1,1))\n",
    "    unq = numpy.unique(new_column)\n",
    "    nrows = numpy.size(new_column)\n",
    "    new_columns = None\n",
    "\n",
    "    for y in unq:\n",
    "        newcol = numpy.zeros((nrows,1),dtype=datatype)\n",
    "        newcol[numpy.where(nc==y)] = 1\n",
    "        if new_columns is None:\n",
    "            new_columns = newcol\n",
    "        else:\n",
    "            new_columns = numpy.concatenate((new_columns,newcol),axis=1)\n",
    "\n",
    "    if data_array is None:\n",
    "        return new_columns\n",
    "    else:\n",
    "        return numpy.concatenate((data_array,new_columns),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "creditdata_info = {\n",
    "        'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/credit.zip',\n",
    "        'signature': '4a91339c69f55e18f3f48004328fbcb7868070b618208fed099920427b084e5e',\n",
    "        'type': 'numpy',\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path = os.getcwd()\n",
    "response = requests.get(creditdata_info['url'], stream=True)\n",
    "dlpath = 'creditdata.zip'\n",
    "with open(dlpath, 'wb') as f:\n",
    "    for chunk in response.iter_content(65536):\n",
    "        f.write(chunk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "zf = zipfile.ZipFile(dlpath)\n",
    "zf.extractall(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "credittxt_path = os.path.join(path,'credit','credit.csv')\n",
    "credit = np.genfromtxt(credittxt_path, dtype=float, delimiter=',', names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "credit_keys = credit.dtype.names\n",
    "datatype = credit.dtype[0]\n",
    "ncols = len(credit_keys)\n",
    "categorical_names = ['edu','married']\n",
    "y_name = 'default'\n",
    "credit_data = None\n",
    "for j in range(0,ncols):\n",
    "    if credit_keys[j] in categorical_names:\n",
    "        credit_data = add_categorical(credit_data,credit[credit_keys[j]],datatype)\n",
    "    elif credit_keys[j] == y_name:\n",
    "        y = credit[y_name].astype(int)\n",
    "    else:\n",
    "        credit_data = add_column(credit_data,credit[credit_keys[j]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Rescale the dataset by subtracting the mean from each column and dividing by the standard deviation\n",
    "datashape = credit_data.shape\n",
    "nrows = datashape[0]\n",
    "cmeans = np.mean(credit_data,0)\n",
    "repmeans = numpy.matlib.repmat(cmeans,nrows,1)\n",
    "mydata = credit_data - repmeans\n",
    "sstds = np.std(mydata,0)\n",
    "repstds = numpy.matlib.repmat(sstds,nrows,1)\n",
    "mydata = np.divide(mydata,repstds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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vPR+8lvNWiqKgUqnQ19fHxsaG/v37s2zZMlxcXGpxetEQSUALIcSfKCqrYM3P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\nzEcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa579003550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualizer = ROCAUC(LinearSVC())\n",
    "visualizer.fit(mydata,y)\n",
    "visualizer.score(mydata,y)\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    ""
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3.0
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}